---
title: Custom model in a PPS
description: How to deploy a custom model in a Portable Prediction Server.

---

# Deploy a custom model in a Portable Prediction Server {: #deploy-a-custom-model-in-a-portable-prediction-server }

Custom inference models allow you to bring your pre-trained models into DataRobot through the Custom Model Workshop. DataRobot supports custom models built in various programming languages, including Python, R, and Java. Once you've created a custom model in DataRobot, you can deploy it to a containerized DataRobot prediction environment called a Portable Prediction Server (PPS). To deploy a custom model to a PPS, you can prepare and test it in the Custom Model Workshop, and then add it to the Model Registry. You can then deploy the custom model using a PPS bundle, which includes everything you need to deploy the model externally while monitoring it alongside models deployed within DataRobot.

To create and deploy a custom model in a PPS, follow the workflow outlined below:

``` mermaid
graph TB
  A[Create a custom model] --> B{Custom model environment?} 
  B --> |Yes|C[Create a custom model environment]
  B --> |No|D[Prepare the custom model];
  C --> D
  D --> E{Test locally?}
  E --> |No|H[Test the custom model in DataRobot]
  E --> |Yes|F[Install the DataRobot Model Runner]
  F --> G[Test the custom model locally]
  G --> H
  H --> I[Register the custom model]
  I --> J{Create an external prediction environment?}
  J --> |No|L[Deploy the custom model to an external prediction environment]
  J --> |Yes|K[Add an external prediction environment]
  K --> L 
  L --> M[Deploy the custom model to a PPS]
```

## Create a custom model {: #create-a-custom-model }

Custom inference models are user-created, pre-trained models (made up of a collection of files) uploaded to DataRobot via the Custom Model Workshop. 

You can assemble custom inference models in either of the following ways:

* Create a custom model *with* the webserver Scoring Code and `start_server.sh` shell file in the model folder. This type of custom model can be paired with a [custom environment](custom-environments#create-a-custom-environment), a [DataRobot drop-in](drop-in-environments) environment, or a custom drop-in environment.

* Create a custom model *without* the webserver Scoring Code and `start_server.sh` shell file in the model folder. This type of custom model *requires* a drop-in environment to provide the webserver Scoring Code and `start_server.sh` file used by the model. You can use the [drop-in environments provided by DataRobot](drop-in-environments), or you can [create a custom drop-in environment](custom-environments#create-a-custom-environment).

[Create a custom model <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](custom-inf-model){ .md-button }

### Optional: Create a custom model environment {: #optional-create-a-custom-model-environment }

If you decide to use a custom environment or a custom drop-in environment, you must create that environment in the Custom Model Workshop. You can reuse any environments you create this way for other custom models. 

You can assemble custom model environments in either of the following ways:

* Create a custom drop-in environment *with* the webserver Scoring Code and a `start_server.sh` file for the model. DataRobot provides several [default drop-in environments](#drop-in-environments) in the Custom Model Workshop.

* Create a custom environment *without* the webserver Scoring Code and `start_server.sh` file. Instead, you must provide the webserver Scoring Code and a `start_server.sh` file in the model folder for the custom model you intend to use with this environment.

[Create a custom model environment <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](custom-environments){ .md-button }

## Prepare the custom model {: #prepare-the-custom-model }

Before adding custom models and environments to DataRobot, you must prepare and structure the files required to run them successfully. The tools and templates necessary to prepare custom models are hosted in the [Custom Model GitHub repository](https://github.com/datarobot/datarobot-user-models){:target="_blank"} ({% include 'includes/github-sign-in.md' %}). Once you verify the model's files and folder structure, you can proceed to test the model.

[Prepare a custom model <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](custom-model-assembly/index){ .md-button }

### Optional: Test locally {: #test-locally }

The [DataRobot Model Runner (DRUM)](https://pypi.org/project/datarobot-drum/){:target="_blank"} is a tool you can use to work locally with Python, R, and Java custom models. It can verify that a custom model can run and make predictions before you add it to DataRobot. However, this testing is only for development purposes, and DataRobot recommends that you use the Custom Model Workshop to test any model you intend to deploy.

[Test a custom model locally <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](custom-local-test){ .md-button }

### Test in DataRobot {: #test-in-datarobot }

Testing the custom model in the Custom Model Workshop ensures that the model is functional before deployment. These tests use the model environment to run the model and make predictions with test data.

!!! note
    While you can deploy your custom inference model without testing, DataRobot strongly recommends that you ensure your model passes testing in the Custom Model Workshop before deployment.

[Test a custom model in DataRobot <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](custom-model-test){ .md-button }

## Register the custom model {: #register-the-custom-model }

After successfully creating and testing a custom inference model in the Custom Model Workshop, you can add it to the Model Registry as a deployment-ready model package.

[Register a custom model <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](custom-model-reg){ .md-button }

## Deploy the custom model externally to a PPS {: #deploy-the-custom-model-to-a-pps }

The custom model Portable Prediction Server (PPS) is a solution for deploying a custom model to an external prediction environment. The PPS is a downloadable tarball containing a deployed custom model, a custom model environment, and the MLOps monitoring agent. Once running, the PPS container serves predictions via the DataRobot API.

### Optional: Add an external prediction environment {: #add-an-external-prediction-environment }

To create an MLOps custom model deployment compatible with the PPS bundle, you must add the custom model package to an external prediction environment. Create an external prediction environment if you don't already have one in DataRobot.

[Add an external prediction environment <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](pred-env){ .md-button }

### Deploy the custom model package to an external prediction environment {: #deploy-the-custom-model-package-to-an-external-prediction-environment }

To create an MLOps custom model deployment with an external prediction environment, deploy your custom model package to an external prediction environment. 

[Deploy a custom model to an external prediction environment <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](deploy-custom-inf-model){ .md-button }

### Deploy the custom model to a PPS {: #deploy-the-custom-model-to-a-PPS }

The custom model PPS bundle is provided for any custom model tagged as having an [external prediction environment](pred-env) in the deployment inventory. You can download the custom model PPS bundle to deploy and monitor the custom model.

[Deploy a custom model to a PPS <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](custom-pps){ .md-button }